Systems | Development | Analytics | API | Testing

When teams start a greenfield application, they often face a simple problem with big consequences. They need to build and test fast, but they do not have production data to work with yet. That gap can slow down development, delay testing, and push teams into risky manual workarounds. In my experience working with App Dev leaders, this is where synthetic data has a clear role. It is not the answer to every test data challenge.

You ask Claude what your MRR was last month. The answer comes back fast, formatted cleanly, stated with total confidence, and completely wrong. Not because Claude is broken, but because it was guessing. Claude has no live connection to your business data by default. It cannot query your CRM, pull from your ad platforms, or check your billing system. So when a marketing manager asks about their numbers, Claude either refuses or generates a plausible-sounding figure based on patterns in its training data.

Website teams often use “testing” and “monitoring” in the same conversation, but they solve different problems. Website testing helps teams catch issues before changes go live. Website monitoring helps teams catch issues after the site is already in production.

From Epic EHR to HL7 files, EDI transactions, FHIR APIs, and secure on-premises data, Fivetran helps healthcare organizations unify fragmented systems into a governed, analytics-ready data foundation.

When Brendan Eich created JavaScript in just 10 days in 1995 for Netscape Navigator, the Date implementation was borrowed directly from Java — and Java had in turn taken it from java.util.Date, which already had its own serious problems. The result was an API that shipped to the world with factory-installed defects.

There’s a moment in every data team’s journey when a bill arrives with unexpected costs. It might be an egress charge you didn’t see coming, a query cost that spiralled when usage spiked, or the quiet realization that migrating your data to a different platform would take months of work and a significant budget. If you’ve been there, you know what vendor lock-in really costs, and it’s not just dollars.

Every few months, a frontier AI lab makes a move that says the quiet part out loud: agents are only as useful as the systems they can reach. The latest example is Anthropic's acquisition of Stainless, the company behind the tooling that turns API specs into SDKs and MCP servers. Anthropic's own framing is direct. Agents need to connect to data and tools, and the path from an API to an agent-ready interface needs to get shorter. We agree. We've been making a version of this argument for two years.

Your CI pipeline worked fine when the app was young. Then the app grew. Features got split into modules. Teams formed around those modules. And somewhere along the way, what used to be a 4-minute build became a 25-minute one. Then 35. Now nobody pushes to main before lunch because the queue is already backed up. If this sounds familiar, you are not alone. According to Google’s 2024 Developer Survey, 83% of Android apps over 500,000 lines of code struggle with build performance.

Picking the wrong test automation framework is a decision that compounds over time. Choose based on your team's stack, not industry hype. Before committing to any framework, run a proof of concept against your actual CI/CD pipeline, not a demo environment. Choosing a test automation framework used to feel like picking a car: there were a few obvious options, most people picked the most popular one, and you lived with the consequences. In 2026, the landscape looks more like a fleet decision.